The present invention relates to a body recognition system employing an image and a recognition method thereof, and a record medium in which a recognition program was recorded, and more particular to an image recognition system for identifying whether an object body photographed in an image is a body registered in a dictionary, or for classifying it into one of a plurality of categories registered in a dictionary, and a recognition method thereof, and a program.
As one example of a conventional recognition system by an image, there is JP-P1993-20442A (FACE IMAGE COLLATING APPARATUS). This is an apparatus for collating a human face image, and an eigenvector of a Fourier spectrum pattern obtained by making a Fourier analysis of a whole image is employed for collation.
Also, in JP-P2000-30065A (PATTERN RECOGNITION APPARATUS AND METHOD THEREOF) was described a method of conducting collation by an angle (mutual subspace similarity) between a subspace to be found from a plurality of input images, and a subspace to be stretched by an image that was registered. A configuration view of one example of the conventional image recognition system is illustrated in FIG. 29. One example of the conventional image recognition system was configured of an image input section 210, an inter-subspace angle calculation section 211, a recognition section 212, and a dictionary storage section 213.
The conventional image recognition system having such a configuration operates as follows. That is, a plurality of the images photographed in plural directions are input by the image input section 210. Next, an angle between subspaces is calculated in the inter-subspace angle calculation section 211.
At first, an input image group is represented by a N-dimensional subspace. Specifically, the whole image is regarded as a one-dimensional feature data to make a principal-component analysis of it, and N eigenvectors are extracted. Dictionary data pre-represented by an M-dimensional subspace are prepared in the dictionary storage section 213 category by category. Further, an angle between a N-dimensional subspace of the input image, and an M-dimensional subspace of the dictionary is calculated in the inter-subspace angle calculation section 211 category by category. The recognition section 212 compares the angles calculated in the inter-subspace angle calculation section 211 to output a category of which an angle is minimum as a recognition result.
By taking a base vector of a dictionary subspace as Φm (m=1, . . . , M), and a base vector of an input subspace as Ψn (n=1, . . . , N), a matrix X having x i j of Equation (1) or Equation (2) as an element is calculated.
(Numerical Equation 1)
                              X          ij                =                              ∑                          m              =              1                        M                    ⁢                                          ⁢                                    (                                                ψ                  i                                ·                                  ϕ                  m                                            )                        ⁢                          (                                                ϕ                  m                                ·                                  ψ                  j                                            )                                                          (        1        )            (Numerical Equation 2)
                              X          ij                =                              ∑                          n              =              1                        N                    ⁢                                          ⁢                                    (                                                ϕ                  i                                ·                                  ψ                  n                                            )                        ⁢                          (                                                ψ                  n                                ·                                  ϕ                  j                                            )                                                          (        2        )            
The square of the cosine of an angle Θ between the subspaces can be found as a maximum eigenvalue of the matrix X. That the angle is small means that the square of the cosine is large. That is, the square of the cosine can be said in other word, i.e. a similarity of a pattern. The maximum eigenvalue of the matrix X is taken as a similarity in the conventional image recognition system to classify it into a category of which the similarity is maximum.
A point common to these conventional image recognition systems lies in that a similarity calculation or a distance calculation at the time of collation is operated only once by employing a feature extracted from the whole image.
However, in the event that a part of an object image was blackishly crushed due to a fluctuation in illumination, and in the event that occlusion occurred (in the event that one part of the object body got under cover), the problem existed that a feature amount acquired from the whole image became abnormal, whereby it was impossible to correctly conduct collation.